# Introduction
This library is a small layer above [**brightway2**](https://brightway.dev/), designed for the definition of **parametric inventories**
with fast computation of LCA impacts, suitable for **monte-carlo** analyis.
**lca-algebraic** provides a set of **helper functions** for :
* **compact** & **human readable** definition of activites :
* search background (tech and biosphere) activities
* create new foreground activites with parametrized amounts
* parametrize / update existing background activities (extending the class **Activity**)
* Definition of parameters
* Fast computation of LCAs
* Computation of monte carlo method and global sensivity analysis (Sobol indices)
# Installation
If you already have Anaconda & Jupyter installed, you can install the library with either **pip** or **conda** :
## Conda
> conda install -c oie-minesparistech lca_algebraic
## PIP
> pip install lca_algebraic
## Pre-packaged installer for Windows
Alternatively, you can download and execute [this installer](https://github.com/oie-mines-paristech/lca_algebraic/releases/download/1.0.0/incer-acv-model-installer.exe). It will setup a full anaconda environment with **Jupyter**,
**Brightway2** and **LCA Algebraic**.
# Usage & documentation
Please refer to the [sample notebook (Markdown)](./example-notebook.md) [(or here as ipynb)](./example-notebook.ipynb).
The full API is [documented here](https://oie-mines-paristech.github.io/lca_algebraic/doc/).
# Licence & Copyright
This library has been developed by [OIE - MinesParistech](http://www.oie.mines-paristech.fr), for the project *INCER-ACV*,
lead by [ADEME](https://www.ademe.fr/).
It is distributed under the **BSD licence**.
# Principles
The main idea of this libray is to move from **procedural definition** of models (slow and prone to errors) to a **declarative / purely functionnal** definition of parametric models (models as **pure functions**).
This enables **fast computation of LCA impacts**.
We leverage the **power of symbolic calculus** provided by the great libary [SymPy](https://www.sympy.org/en/index.html).
We define our model in a **separate DB**, as a nested combination of :
* other foreground activities
* background activities :
* Technical, refering **ecoinvent DB**
* Biopshere, refering **brightway2** biosphere activities
The **amounts** in exchanges are expressed either as **static amounts**, or **symbolic expressions** of pre-defined **parameters**.
Each activity of our **root model** is defined as a **parametrized combination** of the **foreground activities**, which can themselves be expressed by the **background activities**.
When computing LCA for foreground models, the library develops the model as a combination of **only background activities**. It computes **once for all** the impact of **background activities** and compiles a **fast numpy** (vectorial) function for each impact, replacing each background activity by the **static value of the corresponding impact**.
By providing **large vectors** of **parameter values** to those numpy functions, we can compute LCA for **thousands of values** at a time.
# Compatibility with brightway2
Under the hood, the activities we define with **lca-algebraic** are standard **brightway2** activities.
The amounts of exchanges are stored as **float values** or **serialized as string** in the property **formula**.
Parameters are also stored in the **brightay2** projets, making it fully compatible with **brightway**.
Thus, a model defined with **lca-algebraic** is stored as a regular **bw2** projet. We can use **bw2** native support for [parametrized dataset](https://2.docs.brightway.dev/intro.html#parameterized-datasets) for computing LCAs, even if much more slower than the method explain here.
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